@inproceedings{oladepo-etal-2025-predicting,
title = "Predicting Emotion Intensity in Text Using Transformer-Based Models",
author = "Oladepo, Temitope and
Abiola, Oluwatobi and
Abiola, Tolulope and
-, Abdullah and
Muhammad, Usman and
Abiola, Babatunde",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.220/",
pages = "1677--1682",
ISBN = "979-8-89176-273-2",
abstract = "Emotion intensity prediction in text enhances conversational AI by enabling a deeper understanding of nuanced human emotions, a crucial yet underexplored aspect of natural language processing (NLP). This study employs Transformer-based models to classify emotion intensity levels (0{--}3) for five emotions: anger, fear, joy, sadness, and surprise. The dataset, sourced from the SemEval shared task, was preprocessed to address class imbalance, and model training was performed using fine-tuned *bert-base-uncased*. Evaluation metrics showed that *sadness* achieved the highest accuracy (0.8017) and F1-macro (0.5916), while *fear* had the lowest accuracy (0.5690) despite a competitive F1-macro (0.5207). The results demonstrate the potential of Transformer-based models in emotion intensity prediction while highlighting the need for further improvements in class balancing and contextual representation."
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<abstract>Emotion intensity prediction in text enhances conversational AI by enabling a deeper understanding of nuanced human emotions, a crucial yet underexplored aspect of natural language processing (NLP). This study employs Transformer-based models to classify emotion intensity levels (0–3) for five emotions: anger, fear, joy, sadness, and surprise. The dataset, sourced from the SemEval shared task, was preprocessed to address class imbalance, and model training was performed using fine-tuned *bert-base-uncased*. Evaluation metrics showed that *sadness* achieved the highest accuracy (0.8017) and F1-macro (0.5916), while *fear* had the lowest accuracy (0.5690) despite a competitive F1-macro (0.5207). The results demonstrate the potential of Transformer-based models in emotion intensity prediction while highlighting the need for further improvements in class balancing and contextual representation.</abstract>
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%0 Conference Proceedings
%T Predicting Emotion Intensity in Text Using Transformer-Based Models
%A Oladepo, Temitope
%A Abiola, Oluwatobi
%A Abiola, Tolulope
%A -, Abdullah
%A Muhammad, Usman
%A Abiola, Babatunde
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F oladepo-etal-2025-predicting
%X Emotion intensity prediction in text enhances conversational AI by enabling a deeper understanding of nuanced human emotions, a crucial yet underexplored aspect of natural language processing (NLP). This study employs Transformer-based models to classify emotion intensity levels (0–3) for five emotions: anger, fear, joy, sadness, and surprise. The dataset, sourced from the SemEval shared task, was preprocessed to address class imbalance, and model training was performed using fine-tuned *bert-base-uncased*. Evaluation metrics showed that *sadness* achieved the highest accuracy (0.8017) and F1-macro (0.5916), while *fear* had the lowest accuracy (0.5690) despite a competitive F1-macro (0.5207). The results demonstrate the potential of Transformer-based models in emotion intensity prediction while highlighting the need for further improvements in class balancing and contextual representation.
%U https://aclanthology.org/2025.semeval-1.220/
%P 1677-1682
Markdown (Informal)
[Predicting Emotion Intensity in Text Using Transformer-Based Models](https://aclanthology.org/2025.semeval-1.220/) (Oladepo et al., SemEval 2025)
ACL
- Temitope Oladepo, Oluwatobi Abiola, Tolulope Abiola, Abdullah -, Usman Muhammad, and Babatunde Abiola. 2025. Predicting Emotion Intensity in Text Using Transformer-Based Models. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1677–1682, Vienna, Austria. Association for Computational Linguistics.